Herebelow, numerals set forth in square brackets—[ ]—are keyed to the list of references found towards the end of the present disclosure.
In recent years, in a constant effort to effect ongoing improvements in a crowded field of knowledge, there has been a profusion of time-series distance measures and representations. The majority of these attempts to characterize the similarity between sequences is based solely on shape. However, it is becoming increasingly apparent that structural similarities can provide more intuitive sequence characterizations that adhere more tightly to human perception of similarity.
While shape-based similarity methods seek to identify homomorphic sequences using original raw data, structure-based methodologies are designed to find latent similarities, possibly by transforming the sequences into a new domain, where the resemblance can be more apparent.
Generally, an evolving need has been recognized in connection with providing an ever more effective and efficient manner of managing time-series data.